Ray Kurzweil - How to Create a Mind - The Secret of Human Thought Revealed

Let’s use the observations I have discussed above to begin building a brain. We will start by building a pattern recognizer that meets the necessary attributes. Next we’ll make as many copies of the recognizer as we have memory and computational resources to support. Each recognizer computes the probability that its pattern has been recognized. In doing so, it takes into consideration the observed magnitude of each input (in some appropriate continuum) and matches these against the learned size and size variability parameters associated with each input. The recognizer triggers its simulated axon if that computed probability exceeds a threshold. This threshold and the parameters that control the computation of the pattern’s probability are among the parameters we will optimize with a genetic algorithm. Because it is not a requirement that every input be active for a pattern to be recognized, this provides for autoassociative recognition (that is, recognizing a pattern based on only part of the pattern being present). We also allow for inhibitory signals (signals that indicate that the pattern is less likely).

Recognition of the pattern sends an active signal up the simulated axon of this pattern recognizer. This axon is in turn connected to one or more pattern recognizers at the next higher conceptual level. All of the pattern recognizers connected at the next higher conceptual level are accepting this pattern as one of its inputs. Each pattern recognizer also sends signals down to pattern recognizers at lower conceptual levels whenever most of a pattern has been recognized, indicating that the rest of the pattern is “expected.” Each pattern recognizer has one or more of these expected signal input channels. When an expected signal is received in this way, the threshold for recognition of this pattern recognizer is lowered (made easier).

The pattern recognizers are responsible for “wiring” themselves to other pattern recognizers up and down the conceptual hierarchy. Note that all the “wires” in a software implementation operate via virtual links (which, like Web links, are basically memory pointers) and not actual wires. This system is actually much more flexible than that in the biological brain. In a human brain, new patterns have to be assigned to an actual physical pattern recognizer, and new connections have to be made with an actual axon-to-dendrite link. Usually this means taking an existing physical connection that is approximately what is needed and then growing the necessary axon and dendrite extensions to complete the full connection.

Another technique used in biological mammalian brains is to start with a large number of possible connections and then prune the neural connections that are not used. If a biological neocortex reassigns cortical pattern recognizers that have already learned older patterns in order to learn more recent material, then the connections need to be physically reconfigured. Again, these tasks are much simpler in a software implementation. We simply assign new memory locations to a new pattern recognizer and use memory links for the connections. If the digital neocortex wishes to reassign cortical memory resources from one set of patterns to another, it simply returns the old pattern recognizers to memory and then makes the new assignment. This sort of “garbage collection” and reassignment of memory is a standard feature of the architecture of many software systems. In our digital brain we would also back up old memories before discarding them from the active neocortex, a precaution we can’t take in our biological brains.

There are a variety of mathematical techniques that can be employed to implement this approach to self-organizing hierarchical pattern recognition. The method I would use is hierarchical hidden Markov models, for several reasons. From my personal perspective, I have several decades of familiarity with this method, having used it in the earliest speech recognition and natural-language systems starting in the 1980s. From the perspective of the overall field, there is greater experience with hidden Markov models than with any other approach for pattern recognition tasks. They are also extensively used in natural-language understanding. Many NLU systems use techniques that are at least mathematically similar to HHMM.

Note that not all hidden Markov model systems are fully hierarchical. Some allow for just a few levels of hierarchy—for example, going from acoustic states to phonemes to words. To build a brain, we will want to enable our system to create as many new levels of hierarchy as needed. Also, most hidden Markov model systems are not fully self-organizing. Some have fixed connections, although these systems do effectively prune many of their starting connections by allowing them to evolve zero connection weights. Our systems from the 1980s and 1990s automatically pruned connections with connection weights below a certain level and also allowed for making new connections to better model the training data and to learn on the fly. A key requirement, I believe, is to allow for the system to flexibly create its own topologies based on the patterns it is exposed to while learning. We can use the mathematical technique of linear programming to optimally assign connections to new pattern recognizers.

Our digital brain will also accommodate substantial redundancy of each pattern, especially ones that occur frequently. This allows for robust recognition of common patterns and is also one of the key methods to achieving invariant recognition of different forms of a pattern. We will, however, need rules for how much redundancy to permit, as we don’t want to use up excessive amounts of memory on very common low-level patterns.

The rules regarding redundancy, recognition thresholds, and the effect on the threshold of a “this pattern is expected” indication are a few examples of key overall parameters that affect the performance of this type of self-organizing system. I would initially set these parameters based on my intuition, but we would then optimize them using a genetic algorithm.

A very important consideration is the education of a brain, whether a biological or a software one. As I discussed earlier, a hierarchical pattern recognition system (digital or biological) will only learn about two—preferably one—hierarchical levels at a time. To bootstrap the system I would start with previously trained hierarchical networks that have already learned their lessons in recognizing human speech, printed characters, and natural-language structures. Such a system would be capable of reading natural-language documents but would only be able to master approximately one conceptual level at a time. Previously learned levels would provide a relatively stable basis to learn the next level. The system can read the same documents over and over, gaining new conceptual levels with each subsequent reading, similar to the way people reread and achieve a deeper understanding of texts. Billions of pages of material are available on the Web. Wikipedia itself has about four million articles in the English version.

I would also provide a critical thinking module, which would perform a continual background scan of all of the existing patterns, reviewing their compatibility with the other patterns (ideas) in this software neocortex. We have no such facility in our biological brains, which is why people can hold completely inconsistent thoughts with equanimity. Upon identifying an inconsistent idea, the digital module would begin a search for a resolution, including its own cortical structures as well as all of the vast literature available to it. A resolution might simply mean determining that one of the inconsistent ideas is simply incorrect (if contraindicated by a preponderance of conflicting data). More constructively, it would find an idea at a higher conceptual level that resolves the apparent contradiction by providing a perspective that explains each idea. The system would add this resolution as a new pattern and link to the ideas that initially triggered the search for the resolution. This critical thinking module would run as a continual background task. It would be very beneficial if human brains did the same thing.